{"id":"W4385497738","doi":"10.1016/j.joule.2023.07.011","title":"Regeneration of direct air CO2 capture liquid via alternating electrocatalysis","year":2023,"lang":"en","type":"article","venue":"Joule","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Electrolysis; Electrocatalyst; Electrochemistry; Materials science; Electrode; Carbon dioxide; Hydrogen; Chemical engineering; Waste management; Chemistry; Environmental science; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001729201,0.00009258898,0.0001662917,0.0001495008,0.00006864507,0.000009697273,0.0001137561,0.00007517455,0.0001115476],"category_scores_gemma":[0.00003795106,0.00008774491,0.0001107289,0.0005839465,0.0000162148,0.00008949776,0.00003943254,0.00008830309,0.00006404274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005540928,"about_ca_system_score_gemma":0.00002228129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001572579,"about_ca_topic_score_gemma":0.000322918,"domain_scores_codex":[0.9991998,0.00003103087,0.0002311817,0.0001825577,0.0001913503,0.000164059],"domain_scores_gemma":[0.9994876,0.00001809991,0.0001198548,0.0002605772,0.00007674756,0.00003715227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004000742,0.0000425234,0.00006215562,0.00003314546,0.00008522382,0.000007336185,0.0003700956,0.00504822,0.9636875,0.001785671,0.01192965,0.01690852],"study_design_scores_gemma":[0.00008808123,0.00007436919,0.0001151101,0.00001218938,0.00002663579,0.000007748116,0.00006144019,0.002802275,0.9748904,0.0003605163,0.02145378,0.0001074179],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749434,0.0002390046,0.001594865,0.0006799349,0.0002379792,0.0001039274,0.000007157401,0.000617306,0.02157641],"genre_scores_gemma":[0.9933456,0.00008632757,0.0002001585,0.00005604121,0.0002214663,0.00002963601,0.0001548909,0.00002203025,0.005883913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01840212,"threshold_uncertainty_score":0.3578133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009467785250367646,"score_gpt":0.2486960905553514,"score_spread":0.2392283053049838,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}